IBM’s CEO Ginni Rometty has taken a careful approach to handling user data, in line with its focus on enterprise customers. In contrast, Google, Microsoft and Amazon have been hoovers of customer information, causing plenty of controversy over the past year. This has left many analysts to wait for the other foot to drop—first the congressional hearings, then actual regulations. IBM is an interesting case study in that it has much of the same algorithmic and A.I. tech as its data driven peers, yet the tech has been cordoned off behind paywalls, as is the enterprise approach. IBM has been keen on highlighting client ip protection in its products.
One such example is AIEQ, is an A.I. powered ETF which uses IBM’s Watson to sift through thousands of reports on public stocks to find investment opportunities.
Double Edged Sword
As we all know, big data is a powerful tool, as it enables gaining new insight into areas where people were not necessarily thinking about or looking for previously. Although, it can turn into an addiction for companies that have honed their entire workforce into processing and extracting value from constant streams of data. It’s their lifeblood. Increased awareness of privacy concerns and regulatory attention presents a major threat to these data giants. An advantage for IBM, which has built many of its systems with privacy in mind from the ground up. It would certainly be less effected by the recent proposed regulatory oversight.
On the other hand, IBM’s stance on keeping customer IP private means that it might not be able to iterate as fast as it’s data-consuming peers. Much of A.I. today is about running programs through innumerable training exercises. The more data, the smarter the A.I. becomes.
Outlook
So, the key to success for IBM’s “cognitive solutions” will be from leveraging cooperation with enterprise clients. IBM may be able to encourage voluntary disclosure and feedback without compromising its clients intellectual property. Fostering partnerships over customers. Data scientists, and A.I. specialists may be assigned to each cognitive computing client. This could help ensure there is constant, specialized iteration down to the specific use case, not just the industry. Ginni has mentioned using specialists to make sure Watson is “trained” the right way. This takes it a step further in the specialized iterative process.
There have been quite a few complaints from shareholders about Ginni’s large salary, however IBM was already due for a massive overhaul and restructuring by the time she went in. There are indeed massive changes happening right under our noses, something that naturally attracts critics. It’s a tedious and difficult job pivoting such a massive enterprise, something that I believe will start bearing fruit. They might even look back and realize Ginni was worth every penny. Indeed some are starting to ask, is “big blue” due for a comeback?
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